Using a real data series, jackknifing allows a researcher to create a large number of hypothetical cases by resampling the data by eliminating one (or more) case at a time. It is used principally to determine the effect outliers or abberrant cases have on the results of the study. The fundamental procedure is as follows:

Identify the data series of interest. This data series will have n observations.

If desired, repeat steps 3-5, eliminating more than one case at a time.

This method of simulation is particularly useful when the data being studied do not conform to typical distributions, or may contain a lot of uncertainty.

Update:ariels tells me that this is called leave-one-out cross-validation by computer scientists, but that the procedure is not quite. Leave-one-out cross validation is used to estimate the error probabilites, while jackknifing can be used to estimate many different parameters or even distributions.